<html><head><title>Applied Scientist - Berkeley, CA</title></head>
<body><h2>Applied Scientist - Berkeley, CA</h2>
<ul><li>Masters degree in Computer Science, Engineering, Mathematics or related discipline</li><li>PhD in one of above disciplines, OR at least 4 years of experience with machine learning systems</li><li>At least 2 years of experience building web based production software</li><li>Hands on development experience in C++, Java, and Python</li></ul><br/>
Amazon is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

Next Generation of Amazon Search<br/>
<br/>
Amazon is the 4th most popular site in the US (source:<br/>
http://www.alexa.com/topsites/countries/US). Our product search engine,<br/>
one of the most heavily used services in the world, indexes billions of<br/>
products and serves hundreds of millions of customers world-wide. We are<br/>
working on a new initiative to transform our search engine into a<br/>
shopping engine that assists customers with their shopping<br/>
missions. We’re looking at every aspect of search, from query<br/>
understanding to front-end UX, ranking and relevance, indexing and<br/>
tiering and asking how we can make big, step improvements by applying<br/>
advanced Machine Learning (ML) and Deep Learning (DL) techniques. This is a rare opportunity to develop cutting edge ML solutions and apply<br/>
them to a search problem of this magnitude. Some exciting questions that<br/>
we expect to answer over the next few years include:<br/>
<br/>
<ul><li>Can we deeply understand customer intent and personalize their search</li></ul>
experience even when they type broad queries such as “dress” or<br/>
“espresso machine”?<br/>
<br/>
<ul><li>Can we reduce the cost of serving customer queries on Amazon by two</li></ul>
orders of magnitude using ML to predict n-grams and tuples that many<br/>
queries decompose into, apply expensive ranking functions offline to<br/>
identify the most relevant products that match these terms, and index<br/>
these for efficient online retrieval? We expect this to lead to exciting<br/>
research at the intersection of systems and ML.<br/>
<br/>
<ul><li>Can we deeply understand the catalog to surface products that offer</li></ul>
the most value to a customer? The challenge here is that the definition<br/>
of value is subjective and personal, and therefore requires a deeper<br/>
understanding of the customers intent as well as preferences.<br/>
<br/>
<ul><li>Can we increase the experimental velocity of Customer Experience (CX)</li></ul>
experiments by two orders of magnitude? Achieving this will enable us to<br/>
rapidly try various CX treatments, and contextualize the CX based on<br/>
factors such as customer intent and device.<br/>
<br/>
<ul><li>Can we use deep learning to transfer behavioral signals from</li></ul>
frequently purchased products in the head to products in the tail where<br/>
behavioral signals are sparse? The challenge here is the scale, and the<br/>
fact that the head and torso contain only a small fraction of products<br/>
while the tail contains an overwhelmingly large fraction of the products<br/>
in the catalog.<br/>
<br/>
We are looking to hire ML Applied Scientists at all levels, with experience in Search, Personalization, NLP, Systems, ML, DL and UI Design. Internship opportunities are also available throughout the year and we are flexible about duration and start dates. You will be working alongside world-class researchers and engineers to build next generation search systems and will be able to deploy your ML models into production. Our team is proud of its collaborative and open research environment, where long term thinking and risk taking are highly rewarded. We value academic collaborations and encourage our scientists and engineers to participate and publish in top conferences such as NIPS, ICML, KDD, SIGIR and WWW.<br/>
<br/>
Positions are available in the new Amazon office in Berkeley, CA.

. PhD in Computer Science, Electrical Engineering, Statistics, Applied Mathematics or related field.<br/>
<ul><li>Strong Computer Science fundamentals in machine learning, data structures, algorithm design and complexity analysis</li><li>Experience with web-scale data processing</li><li>Strong fundamentals in applied statistics and probability</li><li>Technically deep in the principles of building large-scale machine learning systems</li><li>Self-directed, flexible, goal oriented and strong sense of ownership</li><li>Strong verbal and written communications skills; experience presenting complex technical information, succinctly, to technical and non-technical audiences</li></ul></body>
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